
import csv
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import plotly.express as px
import plotly.graph_objects as go

from cpmm import CPMM, run_experiment


cpmm = CPMM(fee_fraction=0.02)
initial_liquidity = 1000
amount = 10
cpmm.create_event(initial_liquidity, initial_yes_to_no=1)

vals = []
yes_total_payout = 0
for i in range(1, 1001):
    yes_payout = cpmm.buy_token(1, amount)[1]
    cpmm.buy_token(0, amount * 3)
    yes_total_payout += yes_payout
    yes_odds = cpmm.calc_british_odds(yes_payout, amount)

    vals.append([yes_payout,
        yes_odds,
        cpmm.lp_token / cpmm.liquidity,
        yes_total_payout,
        cpmm.fee_pool / cpmm.liquidity,
        cpmm.calc_impermanent_loss() / cpmm.liquidity,
        cpmm.calc_outstanding_token()[1]])

df = pd.DataFrame(data=vals, columns=["yes payout", "yes odds", "total stake", "yes total reward", "fee pool", "impermanent loss", "outstanding token"])

fig_1 = px.line(df, x="total stake", y=["fee pool", "impermanent loss"], title='fees and loss vs total stake. all values are represented as % of initial liquidity including the stake')
display(fig_1)

fig_2 = px.line(df, x="yes odds", y=["fee pool", "impermanent loss"], title='fees and loss vs odds. all values are represented as % of initial liquidity')
fig_2.update_yaxes(ticksuffix=":1")
display(fig_2)
display(df)


# Y axis - shift the odds, X axis turnover as % of liquidity, COLOR - profit/loss scaled as fraction of liquidity

def run_market(initial_liquidity, fee_fraction, max_turnover_fraction, max_yes_odds, max_slippage, fee_to_liquidity_fraction) -> CPMM:
    cpmm = CPMM(fee_fraction=fee_fraction, fee_to_liquidity_fraction=fee_to_liquidity_fraction)
    amount = initial_liquidity * max_slippage
    no_amount = amount * max_yes_odds
    cpmm.create_event(initial_liquidity)
    # how many steps, each steps puts amount for YES and amount * odds into no
    turnover_steps = (max_turnover_fraction * initial_liquidity) // (amount + no_amount) + 1
    # vals = []
    for i in range(1, int(turnover_steps)):
        cpmm.buy_token(1, amount)
        cpmm.buy_token(0, no_amount)
    return cpmm

# cpmm = run_market(1000, 0.02, 400, 8, 0.02)
# # cpmm.calc_impermanent_loss()
# df = cpmm.history_as_dataframe
# df["odds"] = (df["returned tokens"] - df["amount"]) /  df["amount"]
# display(df)

def run_map(initial_liquidity, fee_fraction, max_turnover_fraction, max_yes_odds, max_slippage, fee_to_liquidity_fraction=0):
    profit_mx = []

    x_labels = np.arange(0.2, max_turnover_fraction, max_turnover_fraction / 250.0)
    y_labels = np.arange(1, max_yes_odds, 0.2)
    for max_yes_odds in y_labels:
        vals = []
        for max_turnover_fraction in x_labels:
            cpmm = run_market(initial_liquidity, fee_fraction, max_turnover_fraction, max_yes_odds, max_slippage, fee_to_liquidity_fraction)
            profit = cpmm.fee_pool - cpmm.calc_impermanent_loss()
            vals.append(profit)
        profit_mx.append(vals)

    return profit_mx, x_labels, y_labels

def run_scatter(initial_liquidity, fee_fraction, max_turnover_fraction, max_yes_odds, max_slippage, fee_to_liquidity_fraction=0):
    profit_mx = []

    x_labels = np.arange(0.2, max_turnover_fraction, max_turnover_fraction / 250.0)
    y_labels = np.arange(1, max_yes_odds, 0.2)
    for max_yes_odds in y_labels:
        for max_turnover_fraction in x_labels:
            cpmm = run_market(initial_liquidity, fee_fraction, max_turnover_fraction, max_yes_odds, max_slippage, fee_to_liquidity_fraction)
            profit = cpmm.fee_pool - cpmm.calc_impermanent_loss()
            total_stake = cpmm.lp_token - cpmm.liquidity
            yes_odds = cpmm.calc_british_odds(cpmm.calc_buy(1, initial_liquidity * max_slippage)[0], initial_liquidity * max_slippage)
            profit_mx.append([total_stake, yes_odds, profit])

    return profit_mx

def display_scatter(initial_liquidity, fee_fraction, max_slippage, fee_to_liquidity_fraction, profit_mx):
    df = pd.DataFrame(data=profit_mx, columns=["total stake", "odds", "profit"])
    color_continuous_scale = ["red", "white", "green"]
    fig = px.scatter(df,
        title=f"Profit/Loss: {initial_liquidity} liquidity at 1:1, {fee_fraction*100}% fee with {fee_to_liquidity_fraction*100}% reinvested {max_slippage*100}% max slippage",
        labels=dict(x="Stake as multiple of initial liquidity", y="Odds divergence from 1:1", color="Profit/Loss as % of initial"),
        color_continuous_midpoint=0,
        color_continuous_scale=color_continuous_scale,
        x=df["total stake"] / initial_liquidity,
        y="odds",
        color=df["profit"]*100/initial_liquidity)
    fig.update_yaxes(ticksuffix=":1")
    display(fig)


# profit_mx = run_scatter(10000, 0.02, 50, 8, 0.02)
display_scatter(10000, 0.02, 0.02, 0, profit_mx)
# profit_mx_2 = run_scatter(1000000, 0.02, 50, 8, 0.02)
display_scatter(1000000, 0.02, 0.02, 0, profit_mx_2)
# profit_mx_3 = run_scatter(10000, 0.02, 200, 8, 0.08)
display_scatter(10000, 0.02, 0.08, 0, profit_mx_3)
# profit_mx_4 = run_scatter(10000, 0.02, 50, 8, 0.02, 0.5)
display_scatter(10000, 0.02, 0.02, 0.5, profit_mx_4)

	yes payout	yes odds	total stake	yes total reward	fee pool	impermanent loss	outstanding token
0	19.504892	0.950489	1.0392	19.504892	0.0008	0.019315	39.009811
1	19.889006	0.988901	1.0784	39.393898	0.0016	0.037542	76.547847
2	20.272793	1.027279	1.1176	59.666691	0.0024	0.054761	112.694141
3	20.655834	1.065583	1.1568	80.322525	0.0032	0.071044	147.521669
4	21.037725	1.103772	1.1960	101.360250	0.0040	0.086457	181.097190
...	...	...	...	...	...	...	...
995	39.200000	2.920000	40.0432	38296.386814	0.7968	0.427529	1174.342124
996	39.200000	2.920000	40.0824	38335.586814	0.7976	0.427529	1174.342124
997	39.200000	2.920000	40.1216	38374.786814	0.7984	0.427529	1174.342124
998	39.200000	2.920000	40.1608	38413.986814	0.7992	0.427529	1174.342124
999	39.200000	2.920000	40.2000	38453.186814	0.8000	0.427529	1174.342124

Loss vs. fee - breaking even¶

Profit Loss heat map¶